distribution shift

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law NeurIPS 2020 Performative Prediction ICML 2020 Posterior Re-calibration for Imbalanced Datasets NeurIPS 2020 Pretrained Transformers Improve Out-of-Distribution Robustness ACL 2020 REST: Performance Improvement of a Black Box Model via RL-Based Spatial Transformation AAAI 2020 Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective CVPR 2020 Rethinking Importance Weighting for Deep Learning under Distribution Shift NeurIPS 2020 Robust Federated Learning: The Case of Affine Distribution Shifts NeurIPS 2020 Self-Supervised Learning for Generalizable Out-of-Distribution Detection AAAI 2020 Single Point Transductive Prediction ICML 2020 Stable Learning via Sample Reweighting AAAI 2020 Stable Prediction with Model Misspecification and Agnostic Distribution Shift AAAI 2020 Stochastic Optimization for Performative Prediction NeurIPS 2020 Test-Time Training with Self-Supervision for Generalization under Distribution Shifts ICML 2020 The Effect of Natural Distribution Shift on Question Answering Models ICML 2020 The MAGICAL Benchmark for Robust Imitation NeurIPS 2020 The Pitfalls of Simplicity Bias in Neural Networks NeurIPS 2020 Towards Accurate and Robust Domain Adaptation under Noisy Environments IJCAI 2020 Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD) ICML 2020 Understanding Self-Training for Gradual Domain Adaptation ICML 2020 Addressing Model Vulnerability to Distributional Shifts Over Image Transformation Sets ICCV 2019 Bias Reduction via End-to-End Shift Learning: Application to Citizen Science AAAI 2019 Data-Driven Approach to Multiple-Source Domain Adaptation AISTATS 2019 Distributionally Adversarial Attack AAAI 2019 Distributionally Robust Semi-Supervised Learning for People-Centric Sensing AAAI 2019